London Medical Imaging & AI Centre Accelerates Research with Run:ai
Client
London Medical Imaging & AI Centre
Industry
Healthcare & Medical Research
AI Tech Solution
AI-Powered Imaging and Experiment Management
Solution Provider
Run:ai
Challenge
The London Medical Imaging & Artificial Intelligence Centre for Value-Based Healthcare, a consortium led by King's College London, specializes in developing AI-driven healthcare tools using medical imaging and NHS healthcare data. To advance AI research in computer vision and natural language processing for healthcare, the center faced several challenges: Low GPU utilization, with many resources sitting idle despite high AI workload demand. Inefficient scheduling, where smaller jobs blocked larger, high-priority workloads from executing efficiently. Slow AI experimentation cycles, limiting the speed at which AI models could be trained and validated. To accelerate AI-powered medical research and improve GPU efficiency, the center needed a scalable, AI workload management solution.
Solution
The AI Centre deployed Runa:ais AI workload orchestration platform, transforming its GPU scheduling and resource allocation strategy. By leveraging Runa:ais GPU virtualization technology, the organization was able to: Increase GPU utilization by 110%, dynamically allocating GPUs to AI workloads based on real-time demand. Improve scheduling efficiency, ensuring that large workloads could execute alongside smaller jobs without delays. Enable researchers to run over 300 AI experiments in 40 days, compared to just 162 experiments under previous systems. The self-service GPU allocation model enabled AI researchers to request and receive compute resources on demand, significantly reducing administrative overhead and manual coordination.
Results
By implementing Runa:ais AI workload orchestration platform, the London Medical Imaging & AI Centre achieved breakthrough efficiencies in AI-driven medical research. 31x faster AI experimentation cycles, accelerating AI model development. 2.1x higher GPU utilization, ensuring that compute resources were fully optimized. 1.85x increase in AI experiments completed, leading to faster advancements in AI-powered medical diagnostics. With Runa:ais AI infrastructure optimization tools, the center has successfully enhanced its AI-driven medical research capabilities, unlocking new potential in faster disease detection, personalized therapies, and advanced AI-driven healthcare solutions. Case Study Highlights AI-powered medical research acceleration: AI model training speeds increased 31x, leading to faster breakthroughs. GPU efficiency maximization: AI compute utilization improved by 2.1x, reducing idle GPU time. Increased AI experimentation: The number of completed AI experiments rose by 1.85x, advancing healthcare innovation. Zebra Run ai Hybrid Case Study 2024.pdf PDF https://pages.run.ai/hubfs/PDFs/Case%20Studies/Hybrid%20Case%20Study%202024.pdf
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